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utils.py
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utils.py
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import numpy as np
import nltk
import difflib
import editdistance
import re
from scipy import spatial
import statistics
import string
from nltk.tokenize import sent_tokenize
import gpt_2_simple as gpt2
def preprocess_candidates(candidates):
for i in range(len(candidates)):
candidates[i] = candidates[i].strip()
candidates[i] = '. '.join(candidates[i].split('\n\n'))
candidates[i] = '. '.join(candidates[i].split('\n'))
candidates[i] = '.'.join(candidates[i].split('..'))
candidates[i] = '. '.join(candidates[i].split('.'))
candidates[i] = '. '.join(candidates[i].split('. . '))
candidates[i] = '. '.join(candidates[i].split('. . '))
while len(candidates[i].split(' ')) > 1:
candidates[i] = ' '.join(candidates[i].split(' '))
myre = re.search(r'(\d+)\. (\d+)', candidates[i])
while myre:
candidates[i] = 'UNK'.join(candidates[i].split(myre.group()))
myre = re.search(r'(\d+)\. (\d+)', candidates[i])
candidates[i] = candidates[i].strip()
processed_candidates = []
for candidate_i in candidates:
sentences = sent_tokenize(candidate_i)
out_i = []
for sentence_i in sentences:
if len(
sentence_i.translate(
str.maketrans('', '', string.punctuation)).split()
) > 1: # More than one word.
out_i.append(sentence_i)
processed_candidates.append(out_i)
return processed_candidates
def get_redundancy_score(all_summary):
def if_two_sentence_redundant(a, b):
""" Determine whether there is redundancy between two sentences. """
if a == b:
return 4
if (a in b) or (b in a):
return 4
flag_num = 0
a_split = a.split()
b_split = b.split()
if max(len(a_split), len(b_split)) >= 5:
longest_common_substring = difflib.SequenceMatcher(
None, a, b).find_longest_match(0, len(a), 0, len(b))
LCS_string_length = longest_common_substring.size
if LCS_string_length > 0.8 * min(len(a), len(b)):
flag_num += 1
LCS_word_length = len(a[longest_common_substring[0]:(
longest_common_substring[0] +
LCS_string_length)].strip().split())
if LCS_word_length > 0.8 * min(len(a_split), len(b_split)):
flag_num += 1
edit_distance = editdistance.eval(a, b)
if edit_distance < 0.6 * max(
len(a), len(b)
): # Number of modifications from the longer sentence is too small.
flag_num += 1
number_of_common_word = len([x for x in a_split if x in b_split])
if number_of_common_word > 0.8 * min(len(a_split), len(b_split)):
flag_num += 1
return flag_num
redundancy_score = [0.0 for x in range(len(all_summary))]
for i in range(len(all_summary)):
flag = 0
summary = all_summary[i]
if len(summary) == 1:
continue
for j in range(len(summary) - 1): # for pairwise redundancy
for k in range(j + 1, len(summary)):
flag += if_two_sentence_redundant(summary[j].strip(),
summary[k].strip())
redundancy_score[i] += -0.1 * flag
return redundancy_score
def get_similarity_score(candidates, model):
enc_cands = []
for cand in candidates:
curr_emb = []
for sentence in cand:
curr_emb.append(model.encode(sentence))
enc_cands.append(curr_emb)
scores = []
for i in range(len(candidates)):
cand_score = []
for j in range(len(enc_cands[i][:-1])):
cand_score.append(1 - spatial.distance.cosine(enc_cands[i][j], enc_cands[i][j+1]))
if len(cand_score) > 0:
scores.append(statistics.mean(cand_score))
else:
scores.append(0)
return scores
def get_title_score(candidates, title, model):
scores = []
embeddings = model.encode(candidates)
emb_title = model.encode(title)
for emb in embeddings:
result = 1 - spatial.distance.cosine(emb_title, emb)
scores.append(result)
return scores
def calculate_score(candidates,title,genre,model):
processed_candidates = preprocess_candidates(candidates)
redundancy_score = get_redundancy_score(processed_candidates)
similarity_score = get_similarity_score(processed_candidates,model)
title_score = get_title_score(candidates,title,model)
genre_score = get_title_score(candidates,genre,model)
total_score = []
for i in range(len(similarity_score)):
total_score.append(title_score[i] + genre_score[i] + similarity_score[i]/2 + 3*redundancy_score[i])
'''for i in range(len(candidates)):
print(candidates[i])
print(f'Similarity: {similarity_score[i]/2}')
print(f'Title: {title_score[i]}')
print(f'Genre: {genre_score[i]}')
print(f'Redundancy: {redundancy_score[i]}')
print(f'Total: {total_score[i]}')
print('\n')'''
return total_score
def samples_selector(samples,title,genre,model):
lens = [0]*len(samples)
for i in range(len(samples)):
lens[i] = len(samples[i].split(' '))
for i in range(2):
max_len = np.argmax(lens)
samples.pop(max_len)
lens.pop(max_len)
min_len = np.argmin(lens)
samples.pop(min_len)
lens.pop(min_len)
#Passar titulo, genero e model aqui
scores = calculate_score(samples,title,genre,model)
return samples[np.argmax(scores)]